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                  <h1>Face Recognition</h1>
                
                <h2 id="face-recognition">🔖Face Recognition<a class="headerlink" href="#face-recognition" title="Permanent link">&para;</a></h2>
<ul>
<li>Deep face recognition using imperfect facial data <a href="https://www.sciencedirect.com/science/article/pii/S0167739X18331133" title="FGCS2019">[paper]</a></li>
<li>Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhong_Unequal-Training_for_Deep_Face_Recognition_With_Long-Tailed_Noisy_Data_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a> <a href="https://github.com/zhongyy/Unequal-Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data" title="MXNet">[code]</a></li>
<li><strong>RegularFace</strong>: Deep Face Recognition via Exclusive Regularization <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_RegularFace_Deep_Face_Recognition_via_Exclusive_Regularization_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a></li>
<li><strong>UniformFace</strong>: Learning Deep Equidistributed Representation for Face Recognition <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Duan_UniformFace_Learning_Deep_Equidistributed_Representation_for_Face_Recognition_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a></li>
<li><strong>P2SGrad</strong>: Refined Gradients for Optimizing Deep Face Models <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_P2SGrad_Refined_Gradients_for_Optimizing_Deep_Face_Models_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a></li>
<li><strong>AdaptiveFace</strong>: Adaptive Margin and Sampling for Face Recognition <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_AdaptiveFace_Adaptive_Margin_and_Sampling_for_Face_Recognition_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a></li>
<li><strong>AdaCos</strong>: Adaptively Scaling Cosine Logits for Effectively Learning Deep Face Representations <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_AdaCos_Adaptively_Scaling_Cosine_Logits_for_Effectively_Learning_Deep_Face_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a> <a href="https://github.com/xialuxi/arcface-caffe" title="Caffe">[code1]</a> <a href="https://github.com/4uiiurz1/pytorch-adacos" title="PyTorch">[code2]</a></li>
<li>Low-Rank Laplacian-Uniform Mixed Model for Robust Face Recognition <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Dong_Low-Rank_Laplacian-Uniform_Mixed_Model_for_Robust_Face_Recognition_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a></li>
<li><strong>NoiseFace</strong>: Noise-Tolerant Paradigm for Training Face Recognition CNNs <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Hu_Noise-Tolerant_Paradigm_for_Training_Face_Recognition_CNNs_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a> <a href="https://github.com/huangyangyu/NoiseFace" title="Caffe">[code]</a></li>
<li>Feature Transfer Learning for Face Recognition With Under-Represented Data <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Yin_Feature_Transfer_Learning_for_Face_Recognition_With_Under-Represented_Data_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a></li>
<li><strong>Led3D</strong>: A Lightweight and Efficient Deep Approach to Recognizing Low-Quality 3D Faces <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Mu_Led3D_A_Lightweight_and_Efficient_Deep_Approach_to_Recognizing_Low-Quality_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a> <a href="https://github.com/muyouhang/Led3D" title="NULL">[code]</a> <a href="http://irip.buaa.edu.cn/lock3dface/index.html">[dataset]</a></li>
<li>R3 Adversarial Network for Cross Model Face Recognition <a href="http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_R3_Adversarial_Network_for_Cross_Model_Face_Recognition_CVPR_2019_paper.pdf" title="CVPR2019">[paper]</a>  </li>
<li><strong>MLT</strong>: Face Recognition: A Novel Multi-Level Taxonomy based Survey <a href="https://arxiv.org/abs/1901.00713" title="arXiv2019">[paper]</a></li>
<li><strong>GhostVLAD</strong>: GhostVLAD for set-based face recognition <a href="https://arxiv.org/abs/1810.09951" title="ACCV2018">[paper]</a></li>
<li><strong>DocFace+</strong>: ID Document to Selfie Matching <a href="https://arxiv.org/abs/1809.05620" title="arXiv2018">[paper]</a> <a href="https://github.com/seasonSH/DocFace" title="TensorFlow">[code]</a></li>
<li><strong>2018Survey</strong>: Face Recognition: From Traditional to Deep Learning Methods <a href="https://arxiv.org/abs/1811.00116" title="arXiv2018">[paper]</a></li>
<li><strong>2018Survey</strong>: Deep Facial Expression Recognition: A Survey <a href="https://arxiv.org/abs/1804.08348" title="arXiv2018">[paper]</a></li>
<li><strong>2018Survey</strong>: Deep Face Recognition: A Survey <a href="https://arxiv.org/abs/1804.06655" title="arXiv2018">[paper]</a></li>
<li><strong>SphereFace+(MHE)</strong>: Learning towards Minimum Hyperspherical Energy <a href="https://arxiv.org/abs/1805.09298" title="arXiv2018">[paper]</a> <a href="https://github.com/wy1iu/sphereface-plus" title="Caffe/Matlab">[code]</a></li>
<li><strong>MobileFace</strong>: A face recognition solution on mobile device <a href="https://github.com/becauseofAI/MobileFace">[code]</a></li>
<li><strong>MobileFaceNets</strong>: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices <a href="https://arxiv.org/abs/1804.07573" title="arXiv2018">[paper]</a> <a href="https://github.com/deepinsight/insightface" title="MXNet">[code1]</a> <a href="https://github.com/KaleidoZhouYN/mobilefacenet-caffe" title="Caffe">[code2]</a> <a href="https://github.com/xsr-ai/MobileFaceNet_TF" title="TensorFlow">[code3]</a> <a href="https://github.com/GRAYKEY/mobilefacenet_ncnn" title="NCNN">[code4]</a></li>
<li><strong>FaceID</strong>: An implementation of iPhone X's FaceID using face embeddings and siamese networks on RGBD images. <a href="https://github.com/normandipalo/faceID_beta" title="Keras">[code]</a> <a href="https://towardsdatascience.com/how-i-implemented-iphone-xs-faceid-using-deep-learning-in-python-d5dbaa128e1d" title="Medium">[blog]</a> </li>
<li><strong>InsightFace(ArcFace)</strong>: 2D and 3D Face Analysis Project <a href="https://arxiv.org/abs/1801.07698" title="ArcFace: Additive Angular Margin Loss for Deep Face Recognition(arXiv)">[paper]</a> <a href="https://github.com/deepinsight/insightface" title="MXNet">[code1]</a> <a href="https://github.com/auroua/InsightFace_TF" title="TensorFlow">[code2]</a></li>
<li><strong>AAM-Softmax(CCL)</strong>: Face Recognition via Centralized Coordinate Learning <a href="https://arxiv.org/abs/1801.05678" title="arXiv2018">[paper]</a></li>
<li><strong>AM-Softmax</strong>: Additive Margin Softmax for Face Verification <a href="https://arxiv.org/abs/1801.05599" title="arXiv2018">[paper]</a> <a href="https://github.com/happynear/AMSoftmax" title="Caffe">[code1]</a> <a href="https://github.com/Joker316701882/Additive-Margin-Softmax" title="TensorFlow">[code2]</a></li>
<li><strong>CosFace</strong>: Large Margin Cosine Loss for Deep Face Recognition <a href="https://arxiv.org/abs/1801.09414" title="CVPR2018">[paper]</a> <a href="https://github.com/deepinsight/insightface" title="MXNet">[code1]</a> <a href="https://github.com/yule-li/CosFace" title="TensorFlow">[code2]</a></li>
<li><strong>FeatureIncay</strong>: Feature Incay for Representation Regularization <a href="https://arxiv.org/abs/1705.10284" title="ICLR2018">[paper]</a></li>
<li><strong>CocoLoss</strong>: Rethinking Feature Discrimination and Polymerization for Large-scale Recognition <a href="http://cn.arxiv.org/abs/1710.00870" title="NIPS2017">[paper]</a> <a href="https://github.com/sciencefans/coco_loss" title="Caffe">[code]</a></li>
<li><strong>NormFace</strong>: L2 hypersphere embedding for face Verification <a href="http://www.cs.jhu.edu/~alanlab/Pubs17/wang2017normface.pdf" title="ACM2017 Multimedia Conference">[paper]</a> <a href="https://github.com/happynear/NormFace" title="Caffe">[code]</a></li>
<li><strong>SphereFace(A-Softmax)</strong>: Deep Hypersphere Embedding for Face Recognition <a href="http://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_SphereFace_Deep_Hypersphere_CVPR_2017_paper.pdf" title="CVPR2017">[paper]</a> <a href="https://github.com/wy1iu/sphereface" title="Caffe">[code]</a></li>
<li><strong>L-Softmax</strong>: Large-Margin Softmax Loss for Convolutional Neural Networks <a href="http://proceedings.mlr.press/v48/liud16.pdf" title="ICML2016">[paper]</a> <a href="https://github.com/wy1iu/LargeMargin_Softmax_Loss" title="Caffe">[code1]</a> <a href="https://github.com/luoyetx/mx-lsoftmax" title="MXNet">[code2]</a> <a href="https://github.com/HiKapok/tf.extra_losses" title="TensorFlow">[code3]</a> <a href="https://github.com/auroua/L_Softmax_TensorFlow" title="TensorFlow">[code4]</a> <a href="https://github.com/tpys/face-recognition-caffe2" title="Caffe2">[code5]</a> <a href="https://github.com/amirhfarzaneh/lsoftmax-pytorch" title="PyTorch">[code6]</a> <a href="https://github.com/jihunchoi/lsoftmax-pytorch" title="PyTorch">[code7]</a></li>
<li><strong>CenterLoss</strong>: A Discriminative Feature Learning Approach for Deep Face Recognition <a href="https://ydwen.github.io/papers/WenECCV16.pdf" title="ECCV2016">[paper]</a> <a href="https://github.com/ydwen/caffe-face" title="Caffe">[code1]</a> <a href="https://github.com/pangyupo/mxnet_center_loss" title="MXNet">[code2]</a> <a href="https://github.com/ShownX/mxnet-center-loss" title="MXNet-Gluon">[code3]</a> <a href="https://github.com/EncodeTS/TensorFlow_Center_Loss" title="TensorFlow">[code4]</a></li>
<li><strong>OpenFace</strong>: A general-purpose face recognition library with mobile applications <a href="http://elijah.cs.cmu.edu/DOCS/CMU-CS-16-118.pdf" title="CMU2016">[report]</a> <a href="http://cmusatyalab.github.io/openface/">[project]</a> <a href="https://github.com/cmusatyalab/openface" title="Torch">[code1]</a> <a href="https://github.com/thnkim/OpenFacePytorch" title="PyTorch">[code2]</a></li>
<li><strong>FaceNet</strong>: A Unified Embedding for Face Recognition and Clustering <a href="https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf" title="CVPR2015">[paper]</a> <a href="https://github.com/davidsandberg/facenet" title="TensorFlow">[code]</a></li>
<li><strong>DeepID3</strong>: DeepID3: Face Recognition with Very Deep Neural Networks <a href="https://arxiv.org/abs/1502.00873" title="arXiv2015">[paper]</a> </li>
<li><strong>DeepID2+</strong>: Deeply learned face representations are sparse, selective, and robust <a href="https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sun_Deeply_Learned_Face_2015_CVPR_paper.pdf" title="CVPR2015">[paper]</a></li>
<li><strong>DeepID2</strong>: Deep Learning Face Representation by Joint Identification-Verification <a href="https://papers.nips.cc/paper/5416-deep-learning-face-representation-by-joint-identification-verification.pdf" title="NIPS2014">[paper]</a></li>
<li><strong>DeepID</strong>: Deep Learning Face Representation from Predicting 10,000 Classes <a href="https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Sun_Deep_Learning_Face_2014_CVPR_paper.pdf" title="CVPR2014">[paper]</a></li>
<li><strong>DeepFace</strong>: Closing the gap to human-level performance in face verification <a href="https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf" title="CVPR2014">[paper]</a></li>
<li><strong>LBP+Joint Bayes</strong>: Bayesian Face Revisited: A Joint Formulation <a href="https://s3.amazonaws.com/academia.edu.documents/31414608/JointBayesian.pdf?AWSAccessKeyId=AKIAIWOWYYGZ2Y53UL3A&amp;Expires=1543656042&amp;Signature=k6LefuQnIC2x8gep7yQTxqKgzus%3D&amp;response-content-disposition=inline%3B%20filename%3DBayesian_Face_Revisited_A_Joint_Formulat.pdf" title="ECCV2012">[paper]</a> <a href="https://github.com/cyh24/Joint-Bayesian" title="Python">[code1]</a> <a href="https://github.com/MaoXu/Joint_Bayesian" title="Matlab">[code2]</a> <a href="https://github.com/Glasssix/joint_bayesian" title="C++/C#">[code3]</a></li>
<li><strong>LBPFace</strong>: Face recognition with local binary patterns <a href="https://pdfs.semanticscholar.org/3242/0c65f8ef0c5bd83b14c8ae662cbce73e6781.pdf" title="ECCV2004">[paper]</a> <a href="https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html" title="OpenCV">[code]</a></li>
<li><strong>FisherFace(LDA)</strong>: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection <a href="https://apps.dtic.mil/dtic/tr/fulltext/u2/1015508.pdf" title="TPAMI1997">[paper]</a> <a href="https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html" title="OpenCV">[code]</a></li>
<li><strong>EigenFace(PCA)</strong>: Face recognition using eigenfaces <a href="http://www.cs.ucsb.edu/~mturk/Papers/mturk-CVPR91.pdf" title="CVPR1991">[paper]</a> <a href="https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html" title="OpenCV">[code]</a></li>
</ul>
                
                  
                
              
              
                


              
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